69 results about "Local planning" patented technology

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

The invention discloses a multi-agent path planning method, device and equipment, and the method comprises the steps: dividing a map according to first grids of a preset size, carrying out the global planning of all agents on the map, and generating a global path, the global path being a road section passing through a plurality of first grids from a starting point to a target point; on the basis of each path section of the global path, local planning is conducted on all agents, a local path is generated, and each path section is a road section formed by the center points of every two adjacent first grids; real-time data are obtained by monitoring changes of dynamic obstacles and dynamic targets in a map in real time, and the local path and / or the local path are / is updated and planned according to the real-time data to obtain an updated path. Potential collision risks can be quickly found, obstacle avoidance measures can be taken in time, and the safety of a multi-agent system and the smooth completion rate of tasks are improved.

The invention relates to an air-ground collaborative exploration method in an unknown environment based on three-dimensional Gaussian splashing, and belongs to the field of air-ground robot collaborative exploration in a dangerous environment. The method comprises the following steps: S1, acquiring image data of a to-be-explored area by using an unmanned aerial vehicle, describing a complex scene and a differentiable rendering technology by using three-dimensional Gaussian distribution, accurately estimating a camera pose of the unmanned aerial vehicle and reconstructing a three-dimensional environment in real time by combining a simultaneous localization and mapping (SLAM) system, traversing a space and marking a series of key waypoints; step S2, the ground robot realizes accurate exploration of a local three-dimensional environment by using waypoints provided by the unmanned aerial vehicle and combining a collaborative mechanism of global planning and local planning; and S3, dynamically updating the path plan through the robot in the exploration process, and constructing a fine three-dimensional map of the exploration area. According to the invention, the air-ground robot can collaboratively explore the unknown environment and establish the environment map.

The invention provides an unmanned ship path planning method based on an improved RRT algorithm, and the method comprises the following steps: collecting environment information and depth information around an unmanned ship in real time through a sensor, and fusing the environment information and the depth information, thereby obtaining a planning map; calculating the boundary information of the obstacle in the planning map based on an edge detection algorithm to obtain the boundary information of the obstacle; constructing a heuristic function according to the obstacle boundary information and the path target point of the unmanned ship, and guiding an RRT algorithm to sample through the heuristic function to obtain a first global path; pruning and smoothing the first global path to obtain a second global path; performing local planning according to the second global path and the environment information to obtain a local planning path; and the unmanned ship travels according to the local planning path. Compared with the prior art, the unmanned ship path planning efficiency and quality are effectively improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a cluster unmanned aerial vehicle task dual planning method, which comprises the following steps of: constructing a layered task planning framework comprising a global planning subsystem and a local planning subsystem, and enabling the global planning subsystem to execute task subarea division and task distribution; based on the local planning subsystem, task refinement and path planning are carried out on the unmanned aerial vehicle in each task subarea divided by the global planning subsystem, and information is fed back to the global planning subsystem; based on feedback information, through a global planning subsystem, a task planning strategy of the cluster unmanned aerial vehicle is adjusted again, a layered task planning architecture is adopted to get rid of excessive dependence on a single central control node, a complex task can be decomposed into a plurality of relatively independent sub-tasks, the task planning complexity is reduced, the task calculation amount and planning time are reduced, and the task planning efficiency is improved. The resource of each unmanned aerial vehicle is fully utilized, and the resource utilization rate, the information processing capability and the decision scientificity of the cluster unmanned aerial vehicles are improved.

The invention discloses a photovoltaic panel cleaning trolley laser radar SLAM autonomous navigation system, and belongs to the technical field of autonomous navigation, and the system comprises a map construction module which completes SLAM map construction through a Gmapping algorithm; the positioning module adopts self-adaptive Monte Carlo positioning based on a particle filtering algorithm to realize self positioning of the cleaning trolley; the path planning module comprises global path planning and local path planning; according to the global path planning, global path planning is carried out based on a global cost map according to a specified target point; the local path planning is to adjust a local planning path based on a local cost map and obstacle information in the moving process of the cleaning trolley so as to avoid obstacles. According to the cleaning trolley laser radar SLAM autonomous navigation system for the photovoltaic panel, the cleaning trolley can sense the environment in real time, position the position of the cleaning trolley and construct an accurate map so as to plan a path and avoid obstacles.

The invention provides a mobile robot hierarchical path planning method and system based on deep reinforcement learning, and the method comprises the steps: obtaining a two-dimensional map of a static environment where a mobile robot is located, planning a global path from a current position to a target point based on the map, and extracting sub-target points with key information from the global path according to the generated global path; performing local planning based on the global path and the sub-target points, including: constructing a comprehensive reward function, introducing a space attention mechanism for processing, and identifying a key area in the environment; and obtaining an optimal path through a path smoothing method according to a local planning result.

The invention discloses a park integrated energy system planning method and device considering source load uncertainty, and the method comprises the steps: carrying out the modeling of equipment in a park integrated energy system through setting a modeling strategy, and obtaining a park integrated energy system constraint model; constructing a global optimization model, a stage optimization model and a local optimization model; parameters of the system are initialized through the global optimization model, planning stages are divided, and local adjustment points are set; at the starting point of the first planning stage, calling the stage optimization model to obtain a stage planning scheme; calling the local optimization model at a first local adjustment point in the first planning stage to obtain a local planning scheme of the first local adjustment point; obtaining local planning schemes at the local adjustment points in sequence until the first planning stage is finished; and iteratively circulating until all the planning stages finish planning adjustment. According to the method, the influence of multiple uncertain factors is dealt with in a mode of combining dynamic planning, evaluation and adjustment.

The invention discloses a local planning method and device for an unmanned ship and related components. The method comprises the following steps: acquiring a task route and a current position of the unmanned ship; obtaining a starting point and an ending point according to the task route and the current position, and determining a Frenet coordinate system according to the starting point and the ending point; sampling in time and space on the Frenet coordinate system to obtain sampling points; constructing a transverse planning function and a longitudinal planning function according to the sampling points, and obtaining a plurality of sampling tracks according to the transverse planning function and the longitudinal planning function; evaluating the plurality of sampling trajectories by using a trajectory evaluation function, and selecting the sampling trajectory with the minimum cost as a local route according to an evaluation result; and controlling the unmanned ship to travel according to the local route. According to the method, sampling is carried out in space and time, and the local route is obtained according to the surrounding environment information, so that the unmanned ship can better adapt to a complex water area environment, and the accuracy, adaptability and real-time performance of route planning are improved.

The invention discloses an unmanned aerial vehicle path planning method of an improved particle swarm algorithm based on multi-strategy fusion. The unmanned aerial vehicle path planning method comprises the following steps: 1) constructing a three-dimensional environment model; the method comprises the following steps of (1) building an algorithm fitness function, (2) initializing a population position, (4) initializing a particle swarm algorithm parameter, (5) starting iteration, (6) updating the position, (7) dynamically and adaptively updating the parameter, (8) carrying out a random walk strategy and (9) judging. The method has very strong global planning and local planning capabilities, so that the unmanned aerial vehicle can adapt to various environments to work, and has very strong practicability.

The invention discloses a complex environment-oriented body agent navigation and three-dimensional reconstruction method, and relates to the technical field of body agent navigation, and the method comprises the following steps: collecting environment data around a navigation body, using a fine-grained octree for close-range key areas (narrow slits and dead corners), adopting coarse voxel acceleration for a far area, and adopting coarse voxel acceleration for a near-range key area (narrow slits and dead corners); extracting large planes such as wall surfaces, beam columns and the like by using RANSAC (Random Sample Consensus), and constructing an environment'abstract skeleton 'for navigation reference; establishing a temporary three-dimensional space based on the point cloud of the whole body environment, dividing a plurality of detection planes in the space, performing position correction on an obstacle edge dot matrix in the detection planes, determining a movable range, and generating a safe motion path in a feasible space by applying a local planning algorithm; octree + differential coding is carried out on a local map, and only a current activity area and crisis point cloud are reserved; local full-stack deployment enables navigation to be free of dependence on remote communication, and sensing, mapping and navigation closed loop can still be completed when a link is broken.

The invention discloses a robot path planning method and device, equipment, a medium and a product, and relates to the field of robot obstacle avoidance, and the method comprises the steps: constructing a grid map of a target environment; according to the grid map, adopting an improved A * algorithm to obtain a global planning path; the improved A * algorithm constructs a dynamic heuristic function based on geometric distance and dynamic risk parameters on the basis of the A * algorithm; according to the global planning path, adopting a path decomposition method and an improved dynamic window algorithm to obtain a plurality of local planning paths; the improved dynamic window algorithm constructs an evaluation function based on a cross-modal fusion method and a collision time method on the basis of a dynamic window algorithm; and obtaining a robot path planning result according to all the local planning paths. According to the invention, the global path and local path planning of the robot is realized based on the improved A * algorithm and the improved dynamic window algorithm, the path planning efficiency and safety are improved, and the obstacle avoidance capability of the robot in a complex environment is enhanced.

The invention provides an unmanned aerial vehicle autonomous exploration method based on boundary detection and probability sampling fusion, and the method comprises the steps: firstly generating a high-quality viewpoint through a probability sampling model, and providing precise data support for path planning in an exploration stage; secondly, efficient collaboration of global planning and local planning is achieved by means of a layered planning architecture and a topological path search technology; and finally, through a re-planning mechanism, the robustness and task continuity of the unmanned aerial vehicle in the exploration process in a complex unknown environment are ensured. Aiming at the technical defects that the existing autonomous exploration method is easy to fall into local optimum, low in calculation efficiency, insufficient in track smoothness and the like, the invention provides a solution suitable for an unknown environment, and a space exploration task can be efficiently and quickly completed.

The invention relates to a self-balancing bicycle robot multi-task path planning method, which belongs to the technical field of mobile robot autonomous navigation, and comprises the following steps: constructing an environment grid model containing static obstacle information, and fusing obstacle repulsive force and a Voronoi diagram skeleton to guide and generate an environment safety potential field; receiving a multi-task target point sequence, and calculating an optimal cut-in course angle of each target point; performing global path search in a three-dimensional state space by adopting a forward-backward bimodal primitive to generate a global path meeting incomplete kinematics constraints; the local planning adopts an improved dynamic window algorithm, dynamic stability verification and pruning are carried out in the space of the linear speed and the front wheel steering angle, and an optimal instruction is selected. The technical problems of discontinuous path, dynamic instability, poor obstacle avoidance safety and the like during multi-task operation of the self-balancing bicycle robot are solved, and the operation safety, smoothness and efficiency of the robot in a complex unstructured environment are effectively and remarkably improved.

The invention provides a path planning method of an autonomous mobile robot based on an improved A * algorithm, which improves the efficiency of a global planning algorithm by adjusting the cost weight of a path from a node of the A * algorithm to a target, and then completes local path planning by using the improved DWA algorithm, thereby improving the real-time performance and flexibility of path planning of the autonomous mobile robot. Through fusion of an A * algorithm and a DWA algorithm, in the aspect of global planning, cost functions are adjusted, redundant nodes are reduced, time is shortened, and search efficiency is improved. In the aspect of local planning, the robot reasonably bypasses the obstacle by depending on the distance from the obstacle, thereby avoiding falling into global optimum and improving the moving safety of the robot.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

The invention belongs to the technical field of robots, and particularly relates to a robot local planning method and device, a computer readable storage medium and a robot. The method comprises the following steps: acquiring a local occupation grid map of the robot; generating point cloud data of the robot according to the local occupation grid map; wherein the point cloud data is an intersection point set of each ray taking the central position of the robot as a ray origin and an obstacle area in the locally occupied grid map; performing reinforcement learning training on a preset local planning network model based on the point cloud data to obtain a trained local planning network model; and performing local planning on the robot by using the trained local planning network model to obtain a local planning result of the robot. According to the method and the device, the local occupation grid map can be simplified and converted into the point cloud data, so that the point cloud data can be applied to reinforcement learning of local planning of the robot, and the performance of the local planning of the robot can be improved.

The invention belongs to the technical field of unmanned vehicle intelligent control, and particularly relates to a cooperative control method for unmanned information group dynamic interaction, and the method mainly relates to three main parts of sub-body information group initialization judgment, feature-based group global planning operation and feature-based group local planning operation. The method comprises the following steps: firstly, identifying and separating a daughter information group and extracting and calculating the daughter information group according to limited information which can be obtained by daughters in the group; according to the calculation result, the state of each daughter in the group in different environments can be effectively judged, so that the connection state between the daughters is obtained. On the basis of considering reasonable input and effective interaction of system information, the distance between different daughters is dynamically adjusted by taking group comprehensive performance as a target, so that the global and local planning operation and control process of the unmanned group overall architecture under the action of information interaction is realized.

The invention provides a hybrid local planning method and system based on a dynamic state diagram, and the method comprises the steps: calculating a displacement distance range and an orientation angle range of a target point based on the operation parameters, obtained in real time, of an autonomous mobile robot and a pre-constructed initial dynamic diagram; calculating state nodes based on the target point displacement distance range, the orientation angle range and the initial dynamic graph, and constructing a dynamic state graph through the state nodes; constructing a target path based on the dynamic state diagram and a preset search algorithm to obtain a target point, and obtaining a target linear speed and a target point orientation according to the target point and a preset speed strategy; obtaining a target angular velocity based on the target point and a preset path planning algorithm; and sending the target linear velocity, the target angular velocity and the target point orientation to the autonomous mobile robot, so that the autonomous mobile robot plans a local path. According to the method, the optimal path is dynamically and locally adjusted in real time under kinematics constraints, and the path planning reliability is improved.

In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

The invention discloses a material carrying control method and device, a medium and a product, and relates to the technical field of multi-agent collaboration.The method comprises the steps that a global shortest path is determined through a double-layer FBS model self-closed-loop architecture; carrying materials from an initial position according to a global shortest path, and carrying out planned adjustment in real time in the carrying process to complete a global task target; wherein the planning adjustment process of the rescue material at any current position comprises the following steps: acquiring a local task target at the current position; determining a target planning algorithm at the current position based on the local task target at the current position by using a double-layer FBS model self-closed loop architecture; determining a local planning behavior at the current position according to a target planning algorithm at the current position by using a double-layer FBS model self-closed loop architecture; and carrying the relief goods at the current position based on the local planning behavior at the current position. According to the invention, material carrying control in a complex rescue scene can be realized.

The invention relates to an unmanned sweeper covering type sweeping operation method and system and a medium, and the method comprises the steps: M1, collecting the boundary of a to-be-swept area through a handheld positioning data collection device, importing the boundary into a cloud end, and enabling the precision of positioning data to reach the centimeter level; m2, a cloud global planning algorithm module reads vehicle end parameters and plans a coverage type global path through an algorithm in combination with region boundary information; and M3, the vehicle end local planning algorithm module receives the global path, intercepts the global path as a local planning path according to the current positioning, carries out longitudinal planning, generates a complete planning track, and issues the complete planning track to the control module. According to the method, the problems that an unmanned sweeping vehicle is difficult to realize coverage type sweeping operation and cannot adapt to a complex and changeable urban environment, and a traditional coverage type planning algorithm is long in time consumption are solved, and mutually restricted optimization dimensions such as obstacle avoidance, sweeping blind area elimination and operation efficiency improvement can be processed at the same time.

The invention provides a local planning method and device in wheel-foot robot navigation and electronic equipment. The method comprises the following steps: carrying out sparse processing on a plurality of global path points in navigation of the wheel-foot robot to obtain a plurality of inflection points and a final target point; when the wheel-foot robot reaches the current inflection point, the wheel-foot robot is switched into an in-situ stepping steering mode until the angle difference between the course angle of the wheel-foot robot at the current inflection point and the path direction of the connecting line from the current inflection point to the temporary target point is smaller than or equal to a target preset angle threshold value; the temporary target point is a next inflection point to be reached by the wheel-foot robot or a final target point; and switching to a linear rolling advancing mode until the first distance between the wheel-foot robot and the temporary target point is smaller than or equal to a target preset distance threshold value. When the wheel-foot robot reaches the final target point, the difference between the course angle of the wheel-foot robot and the course angle of the final target point is adjusted to be smaller than or equal to a target preset angle threshold value. Angular velocity and linear velocity decoupling of the wheel-foot robot is achieved, and calculation is simple.

This invention discloses a collaborative path planning method based on multi-constraint improved A* algorithm and a dynamic look-ahead mechanism. It constructs a feature distance field map containing a nonlinear safety potential field, transforming discrete environmental information into continuous safety gradients. An improved A* algorithm, incorporating dynamic adaptive heuristic weights, nonlinear safety potential field costs, and kinematic smoothness constraints, is used to plan the globally optimal path. A velocity-adaptive dynamic look-ahead mechanism is established to calculate dynamic look-ahead points that conform to the current motion state in real time. A multi-objective evaluation function containing a global traction index is introduced into the local planning process to optimize control commands by measuring the deviation between the local trajectory and the global intent. This invention effectively solves the problems of "wall-hugging effect," high computational cost in large-scale environments, and poor coordination between global and local planning in traditional methods, improving the navigation safety and smoothness of robots in complex unstructured scenarios.

The present application provides a space launch mission path planning method, device, electronic device and medium, the method comprising: extracting information from geographic information corresponding to the space launch mission and constructing a geographic topology relationship graph; splitting the geographic topology relationship graph according to the hierarchical relationship between different nodes to obtain multiple geographic topology relationship subgraphs; determining multiple candidate planning paths in each geographic topology relationship subgraph, and screening the multiple candidate planning paths based on the space launch mission performance parameters to determine the local planning path corresponding to the geographic topology relationship subgraph, connecting the first local planning path with the second local planning path to generate the best global planning path. By adopting the above-mentioned space launch mission path planning method, device, electronic device and medium, the problem of long search time of the path planning algorithm when the road network scale is large, the search space and the complexity of the space launch mission are high is solved.

This invention relates to a multi-task path planning method for a self-balancing bicycle robot, belonging to the field of autonomous navigation technology for mobile robots. The method includes: constructing an environmental grid model containing static obstacle information; fusing obstacle repulsion forces and a Venn diagram skeleton to guide the generation of a safe environmental potential field; receiving a sequence of multi-task target points and calculating the optimal heading angle for each target point; performing a global path search in a three-dimensional state space using forward-backward dual-modal primitives to generate a global path that satisfies nonholonomic kinematic constraints; and employing an improved dynamic window algorithm for local planning, performing dynamic stability verification and pruning within the space of linear velocity and front wheel steering angle to select the optimal instruction. This invention solves the technical problems of discontinuous paths, dynamic instability, and poor obstacle avoidance safety in multi-task operations of self-balancing bicycle robots, effectively and significantly improving the safety, smoothness, and efficiency of robot operations in complex unstructured environments.

The present invention discloses a double-layer safety redundant obstacle avoidance path planning method for narrow underground lanes, and the steps are as follows: step 1, the obstacle sensing module uses a laser radar system to monitor the obstacle ahead; step 2, the monitoring result is no obstacle, the vehicle travels along the global path, and the obstacle avoidance local planning module is not enabled; step 3, the obstacle ahead is detected, the obstacle avoidance local planning module generates an obstacle bypassing path, and the trajectory tracking control module executes it; step 4, real-time monitoring of the effectiveness of the local obstacle bypassing path, the path fails, the side parking path generation module generates a side parking path, and the trajectory tracking control module executes the side parking; step 5, real-time monitoring of the local obstacle bypassing path is continuously effective, and the trajectory tracking control module executes continuous detour, the present invention relates to the field of unmanned driving technology. The present invention solves the problem that the traditional path planning method often ignores the flexibility of real-time obstacle avoidance and the safety redundancy of the path, and is difficult to meet the needs of special underground operations.

The present invention provides a trajectory planning method for an unmanned helicopter, including global planning and local planning; in global planning, the sampling-based path planning algorithm, i.e., the RRT* algorithm, is used to obtain a smooth and collision-free initial minimized Snap trajectory, and this trajectory is used as the default path for subsequent local planning; in local planning, the result of global planning is used as the default path for actual flight, a section is taken from the result of global planning as the initial path for flight and a polynomial curve is used to fit the initial path, converting the trajectory planning problem into a quadratic programming problem, and finally decoupling the polynomial coefficients and the segment times, and by solving the segment times, a real-time trajectory is obtained. The present invention can achieve real-time trajectory planning for an unknown environment, ensure autonomous avoidance of obstacles during flight, and generate a dynamically feasible real-time trajectory.

Systems and methods for autonomous-vehicle navigation integrating path planning with a perception network. A Bird's Eye View costmap is generated at runtime using only onboard sensors. No external localization providers are used.

The present invention provides a method for local planning of a mobile robot in a crowd environment based on MPC dynamic game theory, comprising: establishing a dynamic game model under the MPC framework with the robot and surrounding pedestrians as participants; linearizing and approximating the dynamic game model to obtain an approximate dynamic game model, and solving the optimality conditions of the approximate model using the maximum principle; designing an inverse optimal control algorithm based on the optimality conditions, and using the inverse optimal control algorithm to identify weight parameters guiding pedestrian decision-making from real pedestrian trajectories; using the identified approximate dynamic game model as a local planner, combined with robot positioning and pedestrian detection algorithms, to achieve local planning and control of the robot in a dynamic crowd environment. This method enables the mobile robot to achieve excellent local planning and control effects in a dynamic crowd environment, such as quickly reaching the target point, avoiding pedestrians, and saving energy.